Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis.

Tumour content plays a pivotal role in directing the bioinformatic analysis of molecular profiles such as copy number variation (CNV). In clinical application, tumour purity estimation (TPE) is achieved either through visual pathological review [conventional pathology (CP)] or the deconvolution of molecular data. While CP provides a direct measurement, it demonstrates modest reproducibility and lacks standardisation.

HistoPlexer: Histopathology-based Protein Multiplex Generation using Deep Learning.

Multiplexed imaging technologies provide crucial insights into interactions between tumors and their surrounding tumor microenvironment (TME), but their widespread adoption is limited by cost, time, and tissue availability. We introduce HistoPlexer, a deep learning (DL) framework that generates spatially-resolved protein multiplexes directly from histopathology images. HistoPlexer employs the conditional generative adversarial networks with custom loss functions that mitigate slice-to-slice variations and preserve spatial protein correlations.

Towards generative digital twins in biomedical research.

Digital twins in biomedical research, i.e. virtual replicas of biological entities such as cells, organs, or entire organisms, hold great potential to advance personalized healthcare.

CD8 + T-cells restrict the development of peritoneal metastasis and support the efficacy of hyperthermic intraperitoneal chemotherapy (HIPEC).

Multimodal therapy for peritoneal metastasis (PM) including cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) provides long-term survival in highly selected colorectal cancer patients. Mechanisms behind HIPEC are unknown and may include induction of adaptive immunity. We therefore analyzed human PM samples and explored the impact of HIPEC in experimental models.

Single-cell AI-based detection and prognostic and predictive value of DNA mismatch repair deficiency in colorectal cancer.

Testing for DNA mismatch repair deficiency (MMRd) is recommended for all colorectal cancers (CRCs). Automating this would enable precision medicine, particularly if providing information on etiology not captured by deep learning (DL) methods. We present AIMMeR, an AI-based method for determination of mismatch repair (MMR) protein expression at a single-cell level in routine pathology samples.

Deployment of an Artificial Intelligence Histology Tool to Aid Qualitative Assessment of Histopathology Using the Nancy Histopathology Index in Ulcerative Colitis.

Background: Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by increased stool frequency, rectal bleeding, and urgency. To streamline the quantitative assessment of histopathology using the Nancy Index in UC patients, we developed a novel artificial intelligence (AI) tool based on deep learning and tested it in a proof-of-concept trial. In this study, we report the performance of a modified version of the AI tool.

Neoadjuvant immune checkpoint blockade in women with mismatch repair deficient endometrial cancer: a phase I study.

Neoadjuvant immune checkpoint blockade (ICB) has shown unprecedented activity in mismatch repair deficient (MMRd) colorectal cancers, but its effectiveness in MMRd endometrial cancer (EC) remains unknown. In this investigator-driven, phase I, feasibility study (NCT04262089), 10 women with MMRd EC of any grade, planned for primary surgery, received two cycles of neoadjuvant pembrolizumab (200 mg IV) every three weeks. A pathologic response (primary objective) was observed in 5/10 patients, with 2 patients showing a major pathologic response.

B cells critical for outcome in high grade serous ovarian carcinoma.

Recent work has shown evidence for the prognostic significance of tumor infiltrating B cells (B-TIL) in high grade serous ovarian carcinoma (HGSOC), the predominant histological subtype of ovarian cancer. However, it remains unknown how the favorable prognosis associated with B-TIL relates to the current standard treatments of primary debulking surgery (PDS) followed by chemotherapy or (neo-)adjuvant chemotherapy (NACT) combined with interval debulking surgery. To address this, we analyzed the prognostic impact of B-TIL in relationship to primary treatment and tumor infiltrating T cell status in a highly homogenous cohort of HGSOC patients.

Stratification to Neoadjuvant Radiotherapy in Rectal Cancer by Regimen and Transcriptional Signatures.

Unlabelled: Response to neoadjuvant radiotherapy (RT) in rectal cancer has been associated with immune and stromal features that are captured by transcriptional signatures. However, how such associations perform across different chemoradiotherapy regimens and within individual consensus molecular subtypes (CMS) and how they affect survival remain unclear. In this study, gene expression and clinical data of pretreatment biopsies from nine cohorts of primary rectal tumors were combined (N = 826).

Identification and validation of a machine learning model of complete response to radiation in rectal cancer reveals immune infiltrate and TGFβ as key predictors.

Background: It is uncertain which biological features underpin the response of rectal cancer (RC) to radiotherapy. No biomarker is currently in clinical use to select patients for treatment modifications.

Methods: We identified two cohorts of patients (total N = 249) with RC treated with neoadjuvant radiotherapy (45Gy/25) plus fluoropyrimidine.

GILEA: In silico phenome profiling and editing using GAN Inversion.

Background: Modeling heterogeneous disease states by data-driven methods has great potential to advance biomedical research. However, a comprehensive analysis of phenotypic heterogeneity is often challenged by the complex nature of biomedical datasets and emerging imaging methodologies.

Methods: Here, we propose a novel GAN Inversion-enabled Latent Eigenvalue Analysis (GILEA) framework and apply it to in silico phenome profiling and editing.

Immune Phenotype-Genotype Associations in Primary Clear Cell Renal Cell Carcinoma and Matched Metastatic Tissue.

Adjuvant immunotherapy has been recently recommended for patients with metastatic clear cell renal cell carcinoma (ccRCC), but there are no tissue biomarkers to predict treatment response in ccRCC. Potential predictive biomarkers are mainly assessed in primary tumor tissue, whereas metastases (METs) remain understudied. To explore potential differences between genomic alterations and immune phenotypes in primary tumors and their matched METs, we analyzed primary tumors (PTs) of 47 ccRCC patients and their matched distant METs by comprehensive targeted parallel sequencing, whole-genome copy number variation analysis, determination of microsatellite instability, and tumor mutational burden.

CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models.

Batch effects (BEs) refer to systematic technical differences in data collection unrelated to biological variations whose noise is shown to negatively impact machine learning (ML) model generalizability. Here we release CohortFinder (http://cohortfinder.com), an open-source tool aimed at mitigating BEs via data-driven cohort partitioning.

Prediction of recurrence risk in endometrial cancer with multimodal deep learning.

Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant treatment. The current gold standard of combined pathological and molecular profiling is costly, hampering implementation. Here we developed HECTOR (histopathology-based endometrial cancer tailored outcome risk), a multimodal deep learning prognostic model using hematoxylin and eosin-stained, whole-slide images and tumor stage as input, on 2,072 patients from eight EC cohorts including the PORTEC-1/-2/-3 randomized trials.

Prognostic impact and causality of age on oncological outcomes in women with endometrial cancer: a multimethod analysis of the randomised PORTEC-1, PORTEC-2, and PORTEC-3 trials.

Background: Numerous studies have shown that older women with endometrial cancer have a higher risk of recurrence and cancer-related death. However, it remains unclear whether older age is a causal prognostic factor, or whether other risk factors become increasingly common with age. We aimed to address this question with a unique multimethod study design using state-of-the-art statistical and causal inference techniques on datasets of three large, randomised trials.

Image-based consensus molecular subtyping in rectal cancer biopsies and response to neoadjuvant chemoradiotherapy.

The development of deep learning (DL) models to predict the consensus molecular subtypes (CMS) from histopathology images (imCMS) is a promising and cost-effective strategy to support patient stratification. Here, we investigate whether imCMS calls generated from whole slide histopathology images (WSIs) of rectal cancer (RC) pre-treatment biopsies are associated with pathological complete response (pCR) to neoadjuvant long course chemoradiotherapy (LCRT) with single agent fluoropyrimidine. DL models were trained to classify WSIs of colorectal cancers stained with hematoxylin and eosin into one of the four CMS classes using a multi-centric dataset of resection and biopsy specimens (n = 1057 WSIs) with paired transcriptional data.

Domain generalization across tumor types, laboratories, and species - Insights from the 2022 edition of the Mitosis Domain Generalization Challenge.

Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This task is challenging for algorithms and human experts alike, with deterioration of algorithmic performance under shifts in image representations. Considerable covariate shifts occur when assessment is performed on different tumor types, images are acquired using different digitization devices, or specimens are produced in different laboratories.

Prognostic and Predictive Value of Immunoscore in Stage III Colorectal Cancer: Pooled Analysis of Cases From the SCOT and IDEA-HORG Studies.

Purpose: Immunoscore (IS) is prognostic in stage III colorectal cancer (CRC) and may predict benefit of duration (6 3 months) of adjuvant infusional fluorouracil, leucovorin, and oxaliplatin (FOLFOX) chemotherapy. We sought to determine IS prognostic and predictive value in stage-III CRC treated with adjuvant FOLFOX or oral capecitabine and infusional oxaliplatin (CAPOX) in the SCOT and IDEA-HORG trials.

Methods: Three thousand sixty-one cases had tumor samples, of which 2,643 (1,792 CAPOX) were eligible for IS testing.

Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer.

Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than gene-level data, we make use of gene ontology and biological activation state information for initial molecular class discovery. In doing so, we defined three pathway-derived subtypes (PDS) in CRC: PDS1 tumors, which are canonical/LGR5 stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1 stem-rich, with elevated stromal and immune tumor microenvironmental lineages; and PDS3 tumors, which represent a previously overlooked slow-cycling subset of tumors within CMS2 with reduced stem populations and increased differentiated lineages, particularly enterocytes and enteroendocrine cells, yet display the worst prognosis in locally advanced disease.

SST-editing: in silico spatial transcriptomic editing at single-cell resolution.

Motivation: Generative Adversarial Nets (GAN) achieve impressive performance for text-guided editing of natural images. However, a comparable utility of GAN remains understudied for spatial transcriptomics (ST) technologies with matched gene expression and biomedical image data.

Results: We propose In Silico Spatial Transcriptomic editing that enables gene expression-guided editing of immunofluorescence images.

Multiplex analysis of intratumoural immune infiltrate and prognosis in patients with stage II-III colorectal cancer from the SCOT and QUASAR 2 trials: a retrospective analysis.

Background: Tumour-infiltrating CD8 cytotoxic T cells confer favourable prognosis in colorectal cancer. The added prognostic value of other infiltrating immune cells is unclear and so we sought to investigate their prognostic value in two large clinical trial cohorts.

Methods: We used multiplex immunofluorescent staining of tissue microarrays to assess the densities of CD8, CD20, FoxP3, and CD68 cells in the intraepithelial and intrastromal compartments from tumour samples of patients with stage II-III colorectal cancer from the SCOT trial (ISRCTN59757862), which examined 3 months versus 6 months of adjuvant oxaliplatin-based chemotherapy, and from the QUASAR 2 trial (ISRCTN45133151), which compared adjuvant capecitabine with or without bevacizumab.